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Dealing with uncertainty in model updating for damage assessment: A review

机译：在损害评估的模型更新中处理不确定性：回顾

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摘要

In structural engineering, model updating is often used for non-destructive damage assessment: by calibrating stiffness parameters of finite element models based on experimentally obtained (modal) data, structural damage can be identified, quantified and located. However, the model updating problem is an inverse problem prone to ill-posedness and ill-conditioning. This means the problem is extremely sensitive to small errors, which may potentially detract from the method's robustness and reliability. As many errors or uncertainties are present in model updating, both regarding the measurements as well as the employed numerical model, it is important to take these uncertainties suitably into account. This paper aims to provide an overview of the available approaches to this end, where two methods are treated in detail: a non-probabilistic fuzzy approach and a probabilistic Bayesian approach. These methods are both elaborated for the specific case of vibration-based finite element model updating for damage assessment purposes.
机译：在结构工程中，模型更新通常用于无损破坏评估：通过基于实验获得的（模态）数据校准有限元模型的刚度参数，可以识别，量化和定位结构破坏。但是，模型更新问题是一个容易引起不适和不适的逆问题。这意味着该问题对小错误极为敏感，可能会损害该方法的鲁棒性和可靠性。由于模型更新中存在许多错误或不确定性，无论是关于测量还是所采用的数值模型，重要的是要适当考虑这些不确定性。本文旨在概述实现此目的的可用方法，其中详细介绍了两种方法：非概率模糊方法和概率贝叶斯方法。这些方法都针对基于振动的有限元模型更新的特定情况进行了详细阐述，以进行损伤评估。

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